Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.

临床医生将大量时间花在患者遭遇的临床记录上，这通常会影响护理质量和临床医生满意度，并导致医生精疲力尽。人工智能（AI）和机器学习（ML）的进步打开了使用数字抄写员自动化临床文档的可能性，它使用语音识别消除了临床医生或医学抄写员的手动文档。但是，由于临床环境和临床对话的复杂性，开发数字抄写员会遇到很多问题。本文确定并讨论了与在临床环境中开发基于语音的自动化文档相关的主要挑战：录制高质量音频，使用语音识别将音频转换为转录本，从会话数据中诱导主题结构，